Object detection with false positive filtering

ABSTRACT

Embodiments of this invention relate to detecting and blurring images. In an embodiment, a system detects objects in a photographic image. The system includes an object detector module configured to detect regions of the photographic image that include objects of a particular type at least based on the content of the photographic image. The system further includes a false positive detector module configured to determine whether each region detected by the object detector module includes an object of the particular type at least based on information about the context in which the photographic image was taken.

This application claims the benefit of U.S. patent application Ser. No.12/453,432, filed May 11, 2009, which claims the benefit of ProvisionalPat. Appl. No. 61/158,958, filed Mar. 10, 2009, which is incorporated byreference herein in its entirety.

BACKGROUND

1. Field of the Invention

Embodiments of this invention are generally related to imagerecognition.

2. Related Art

Recent advances in computer networking and processing make images easilyaccessible. However, public access of images, especially imagescontaining human faces, raises privacy concerns. In one example, theSTREETVIEW service, available from Google, Inc., provides street-levelpanoramic photographs in major metropolitan areas. The photographsinclude images of people's faces and license plates. Putting un-obscuredimages of faces and license plates online may raise privacy concerns.

To protect privacy, some previous efforts have attempted to obscureobjects, such as faces, in images. Regions in images that include facescan be detected and then obscured. These face detection algorithms canbe tuned for high recall to increase the likelihood that every face isdetected. However, when these algorithms are tuned for high-recall, thenumber of false positives (regions that do not actually include a face)may increase.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated herein and form a partof the specification, illustrate embodiments of the present inventionand, together with the description, further serve to explain theprinciples of the invention and to enable a person skilled in thepertinent art to make and use the invention.

FIG. 1 is a diagram illustrating a system for detecting and blurringobjects, such as faces and license plates, in images, according to anembodiment of the present invention.

FIG. 2 is a flowchart illustrating a method for detecting and blurringfaces in images, which may be used in operation of the system in FIG. 1.

FIG. 3 is a flowchart illustrating a method for detecting and blurringlicense plates in images, which may be used in operation of the systemin FIG. 1.

FIG. 4 is a diagram illustrating an example of detecting and blurringlicense plates in images.

The drawing in which an element first appears is typically indicated bythe leftmost digit or digits in the corresponding reference number. Inthe drawings, like reference numbers may indicate identical orfunctionally similar elements

DETAILED DESCRIPTION

To protect privacy in embodiments of the present invention, objects,such as faces and license plates, are detected and blurred. Inembodiments, a face may be detected by a face detection algorithm tunedfor a high recall to detect a large number of image regions. The imageregions are again processed to check for false positives. When checkingfor false positives, information about the context of the images may beused. Finally, the image regions that are not recognized as falsepositive are blurred.

In the detailed description of the invention that follows, references to“one embodiment”, “an embodiment”, “an example embodiment”, etc.,indicate that the embodiment described may include a particular feature,structure, or characteristic, but every embodiment may not necessarilyinclude the particular feature, structure, or characteristic. Moreover,such phrases are not necessarily referring to the same embodiment.Further, when a particular feature, structure, or characteristic isdescribed in connection with an embodiment, it is submitted that it iswithin the knowledge of one skilled in the art to affect such feature,structure, or characteristic in connection with other embodimentswhether or not explicitly described.

FIG. 1 is a diagram illustrating a system 100 for detecting and blurringobjects, such as faces and license plates, in images, according to anembodiment of the present invention. System 100 includes an imagedatabase of unprocessed images (or raw images), raw image database 102.Raw image database 102 is coupled to a processing pipeline server 110,which is further coupled to a blurred image database 120. Processingpipeline server 110 includes an object detector module 104, a falsepositive detector module 130, and an image blurring module 106.

In general, system 100 operates as follows. Processing pipeline server110 receives a photographic image from raw image database 102. Objectdetector module 104 scans for regions of the photographic image thatinclude objects of a particular type, like faces or license plates.Object detector module 104 sends the detected regions to false positivedetector module 130. False positive detector module 130 may determine aset of features relating to each region detected and may evaluate theset of features to determine whether the region is a false positive. Ifthe region is not a false positive, image blurring module 106 may blurthe region of the image. False positive detector module 130 and imageblurring module 106 may repeatedly evaluate and blur each regionidentified by object detector module 104. Processing pipeline server 110then stores the blurred image in blurred image database 120. Blurredimage database 120 may be accessible to users of the web, for examplethrough a web server via one or more networks (not shown). In this way,identifying information of individuals in the photographic images may beobscured in the images that are ultimately available to end users. Thus,the privacy of the individuals is protected. Each of the components ofsystem 100 is described generally below and in more detail with respectto FIGS. 2 and 3.

Raw image database 102 includes unprocessed photographic images. Thephotographic images can include, but are not limited to, portions ofpanoramic images, such as street level panoramas. The street-levelpanoramas may be collected from a moving vehicle using a custom camerarosette system mounted on a vehicle. The camera rosette may include nine5-megapixel cameras, with eight of the cameras aimed roughly parallel tothe ground in a radial configuration and one fisheye lens pointedupwards. The cameras may be synchronized to capture images that can bestitched together into a panorama. In one example, these images can behigh-resolution image stitched together to form a panorama having ahigh-resolution too. The set of images that can be stitched togetherinto a panorama may be referred to as a panorama set. In an embodiment,the images captured by the upward facing camera may be unlikely tocapture identifiable faces or license plates. For that reason, images orportions of panoramas taken from that camera may not be blurred byprocessing pipeline server 110.

Processing pipeline server 110 receives an image from raw image database102. In an example, processing pipeline server 110 may retrieve theimage, for example, using an SQL select statement. Alternatively, rawimage database 102 may push the image to processing pipeline server 110to reduce processing work.

When an image is received by processing pipeline server 110, objectdetector module 104 detects regions of the photographic image thatinclude objects of a particular type, such as faces or license plates.To detect the regions, object detector module 104 may analyze thecontent of the photographic image. In one embodiment of the invention,the sensitivity of object detector module 104 is adjusted for highrecall, perhaps at the expense of precision. As result, the likelihoodthat objects detector module 104 misses an object is low, but thelikelihood that the regions include false positives may increase. Asdescribed in more detail below, object detector module 104 may includetwo detectors: one configured for high-recall, low-precision, andanother configured for a balance between recall and precision.

Object detector module 104 may use a sliding window algorithm to detectobjects. For example, object detector module 104 may use a standardsliding-window approach. Object detector module 104 may use a linearcombination of heterogeneous set of feature detectors such as Gaborwavelets. The feature detectors may vary in complexity and requiredcomputing resources. When all scales are processed, the remainingwindows may be filtered and merged according to their score and thenumber of overlapping boxes. This example is merely illustrative. Otherobject detection techniques may be used as would be recognized by aperson of skill in the art given this description.

Object detector module 104 may include three separate detectors eachwith a sliding window algorithm configured to detect objects withdifferent profile angles (pan or yaw) of, for example, 0-30, 30-60, and60-90 degrees.

Object detector module 104 may also include a landmarker, whichpinpoints facial feature locations within a region. Features extractedat those locations may be used to obtain a refined score that indicatesthe probability of a face being present.

As mentioned earlier, object detector module 104 may have a high recallrate. To attain the high recall rate, object detector module 104 may,for example, be trained with a learning algorithm. Further, objectdetector module 104 may have an increased contrast sensitivity to catchlower-contrast faces. Increased contrast sensitivity may help, forexample, detect faces that are behind glass. Also, object detectormodule 104's training set (e.g., the images used to training its machinelearning algorithm) may include faces from low-resolution street-levelimagery, e.g. about 200 low resolution faces.

Once object detector module 104 determines the regions, object detectormodule 104 passes each region to false positive detector module 130. Inan alternative embodiment, the regions may be stored is a database, andfalse positive detector module 130 may read the regions from thedatabase. False positive detector module 130 determines whether eachregion detected by the object detector module is a false positive. Todetermine whether a region is a false positive, false positive detectormodule 130 may use information about the context in which thephotographic image was taken. For example, the context information mayinclude information indicating which camera in the camera rosette tookthe image, such as an index of the camera or information about vehiclesdetected in the image.

In an embodiment, to determine whether a region is a false positive,false positive detector module 130 may include several components: asimple feature generator module 132, composite feature generator module136, simple false positive detector module 134, and composite falsepositive detector module 138. Simple feature generator module 132 andcomposite feature generator module 136 may generate the inputs forsimple false positive detector module 134 and composite false positivedetector module 138. Using the inputs, both composite false positivedetector module 138 and simple false positive detector module 134 may beconfigured to determine a score that corresponds to a likelihood thatthe region does not include an object of a particular type, such as aface or license plate. However, composite false positive detector module138 may require more computing resources, such as processor cycles andmemory, and may use more inputs than simple false positive detectormodule 134. For this reason, composite false positive detector module138 may be more accurate than simple false positive detector module 134.

In general, false positive detector module 130 may operate as follows.Simple feature generator module 132 may generate a feature vector. Thefeature vector may include one or more of the following:

(a) information describing the shape of the region generated by theobject detector module (e.g., the left, right, bottom and top edges ofthe region, measured in pixels relative to the upper-left corner of theimage, the ratio of the height to the bottom edge of the box, the areaof the box in pixels, or the height and width of the box measured inpixels);

(b) information describing the appearance of the object in the regiongenerated by the object detector module (e.g., the pose angle of a face,such as frontal. 30 degrees profile and 60 degrees profile, or theorientation of a license plate such as frontal or slanted);

(c) information indicating which camera in the camera rosette took theimage (e.g., an index corresponding to the camera); and

(d) a score determined by object detector module 104 (e.g., a score thatcorresponds to a confidence that the region includes an object of theparticular type, a score of a landmarking stage of object detectormodule 104 that determines the likely positions of the eyes, nose, andmouth of the face within the box).

As mentioned at (a), the feature vector can include a ratio of theheight to the bottom edge of the box. The ratio of the box height to boxbottom may capture whether the real-world, three-dimensional size of theobject indicated by a region is reasonable, given the examples seen inthe training set. The ratio and the three-dimensional may relate to eachother according to the equation:

${\frac{{\hat{\upsilon}}_{1}}{{\hat{\upsilon}}_{1} - {\hat{\upsilon}}_{2}} = \frac{y_{c}}{y}},$

where ν₁ is the bottom edge of the object, ν₂ is the top edge of theobject, ν₁−ν₂ is a height of the object, y_(c) is the height of thecamera, and y is the height of the plane of the bottom edge of theobject. In an embodiment, the camera may be affixed to a vehicle at aconstant height. Further, in the case where faces are detected, y may beestimated based on an average human height in a training set. Similarly,y may be estimated based on an average height of a license plate. Inthis embodiment, y_(c)/y may simply be calculated as:

$\frac{{\hat{\upsilon}}_{1}}{{\hat{\upsilon}}_{1} - {\hat{\upsilon}}_{2}},$perhaps with a coefficient offset.

Thus, the ratio of the height to the bottom edge of the box correspondsto the three-dimensional structure of the scene. Using the ratio in thefeature vector adds knowledge of the scene.

Based on the feature vector, simple false positive detector module 134may determine a score that corresponds to a likelihood that the regiondoes not include an object of a particular type, such as a face orlicense plate. If the score is below a threshold, image blurring module106 does not blur the region and the processing for that region ends. Inthis way, the region is identified as including a face using only thelesser computing power required of simple false positive detector module134. Simple false positive detector module 134 rejects many of the falsepositives without performing more resource-intensive computingoperations, for example operations that involve the pixels of the box,such as calculating color statistics or using a color histogram model orconvolutional neural network.

If the score exceeds the threshold, then composite feature generatormodule 136 determines another feature vector. This second feature vectormay include all the element of the simple feature vector in (a)-(d)above, plus one or more of the following:

(e) information describing an overlap between the region and otherregions in the photographic image detected by the object detectormodule;

(f) a face color probability determined using a histogram model trainedon human labeled faces;

(g) an aggregate color statistic determined based on the color hue,saturation, and value within the region;

(h) a value determined using a third artificial neural networkconfigured to determine a likelihood that the region includes an objectof the particular type based on a grayscale version of the region of theimage; and

(i) data from a second object detector module that is not trained forhigh-recall, (e.g., a score that corresponds to a confidence that theregion includes an object of the particular type or a score of alandmarking stage of second object detector module that determines thelikely positions of the eyes, nose, and mouth of the face within thebox).

As mentioned at (f), the second feature vector may include a face colorprobability determined using a histogram model. An example ofdetermining a face color histogram is described in M. J. Jones and J. M.Rehg., “Statistical color models with application to skin detection,”IEEE Conference of computer vision and pattern recognition, pages274-280, 1999. The histogram model may be developed using a large number(e.g. about 100,000) of high resolution images. The images may be markedfor non-skin pixels. Using the pixels, a histogram may be generated. Thehistogram model may be smoothed out using, for example, expectationmaximization. The histogram model may further bc normalized for changesin illumination and weather condition as described in Terrillon et al.“Comparative performance of different skin chrominance models andchrominance spaces for the automatic detection of human faces in colorimages.” In Proc. IEEE Automatic face and gesture recognition, 2000.

In an example, the color histogram may be built a 128-by-128-bin colorhistogram in hue-saturation space from the pixels of primary detectionregions that overlap at least 10% with a ground-truth face region. Todifferentiate between face pixels and non-face pixels, color histogrammay also include pixels from detection regions that were not labeled asface.

Using the histogram model, the likelihood that a given pixel is skin(P(face|color)) can be determined. Applying the histogram model to eachpixel in a region, the second feature vector may include the averageprobability of skin per pixel. A classifier may classify a pixel as skinif the histogram model says that it is more likely skin than not skin.The second feature vector may also include the percentage of pixelsclassified as skin.

As mentioned at (h), the second feature vector may include a valuedetermined using a artificial neural network. The artificial neuralnetwork may be a convolutional neural net that uses local receptivefields and shared weights. The convolutional neural net may have a deeparchitecture that includes two convolutional layers and two sub-samplinglayers. The convolutional neural net may be trained using a constructivelayer addition that generally trains one layer at a time. A grayscaleversion of the region of the image may be inputted into theconvolutional neural net to yield a score included in the second featurevector.

Based on this second feature vector, composite false positive detectormodule 138 may determine a score that corresponds to a likelihood thatthe region includes an object of a particular type. If the score isbelow a threshold, image blurring module 106 does not blur the region.Otherwise, image blurring module 106 blurs the region.

In an alternative embodiment, false positive detector module 130 mayonly have composite feature generator module 136 and composite falsepositive detector module 138. In that embodiment composite featuregenerator module 136 may generate all the feature inputs needed bycomposite false positive detector module 138. Composite false positivedetector module 138 may determine a score based on the features. If thescore exceeds a threshold, then composite false positive detector module138 may determine that the region includes an object of the particulartype. This embodiment may have greater accuracy while using additionalcomputing resources.

Both simple false positive detector module 134 and composite falsepositive detector module 138 may include neural networks. In anembodiment, simple false positive detector module 134 may include afully-connected neural network with 19 input nodes, two output nodes,and two layers of hidden nodes. In contrast, composite false positivedetector module 138 may include a fully-connected neural network with 58nodes: 24 input nodes, two output nodes, and two hidden layers of 16nodes each. For both, the final score from the neural network may beread from the second of the two output nodes. The use of neural networksis merely illustrative. Other algorithms, such as machine learningalgorithms like a support vector machine or a Bayes network, may be usedas would be recognized by a person skilled in the art given thisdescription.

Simple false positive detector module 134 and composite false positivedetector module 138 may be trained. To build the training set, objectdetector module 104 may identify regions, and a human may manually labelthe regions that include the objects of a particular type, e.g. faces orlicense plates. Each region in the training set may be applied to simplefeature generator module 132 and composite feature generator module 136to determine feature vectors as described above.

Simple false positive detector module 134 and composite false positivedetector module 138 may be trained using back-propagation with astochastic gradient descent on the cross entropy. To improve accuracyfor large image regions, the cross entropy function may incorporate thearea of the region. To prevent overtraining, weight decay may be usedfor regularization.

Image blurring module 106 may implement several blurring algorithms aswould be known to a person skilled in the art given this description. Inan embodiment, the blurring algorithm may be irreversible to protectprivacy, but may not be distracting to a user. Examples of blurringalgorithms are described in U.S. patent application Ser. No. 12/078,464,incorporated herein by reference.

Each of object detector module 104, false positive detector module 130,simple false positive detector module 134, composite false positivedetector module 138, composite feature generator module 136 and simplefeature generator module 132 may be implemented in hardware, software,firmware or any combination thereof.

Processing pipeline server 110 (and its component modules 132-138) maybe implemented on any type of computing device. Such computing devicecan include, but is not limited to, a personal computer, mobile devicesuch as a mobile phone, workstation, embedded system, game console,television, set-top box, or any other computing device. Further, acomputing device can include, but is not limited to, a device having aprocessor and memory for executing and storing instructions. Softwaremay include one or more applications and an operating system. Hardwarecan include, but is not limited to a processor, memory and graphicaluser interface display. The computing device may also have multipleprocessors and multiple shared or separate memory components. Forexample, the computing device may be a clustered computing environmentor server farm.

FIG. 2 is a flowchart illustrating a method 200 for detecting andblurring faces in images, which may be used in operation of system 100.Method 200 represents only one illustrative embodiment incorporatingboth a simple neural network and a composite neural network. Asmentioned earlier, in other embodiments, the initial screening with asimple neural network may be foregone, and only a composite neuralnetwork may be used.

Method 200 begins by detecting regions in an image that includes facesat step 202. As mentioned earlier, two face detectors may be used: aprimary detector configured for high recall, perhaps at the expense ofprecision, and a secondary detector configured for to have a lesserrecall with a higher precision. The primary detector detects regionsillustrated in a box 206, and the secondary detector detects regionsillustrated in a box 204. As the primary detector has a higher recallrate, box 206 shows more regions than box 204. For example, box 206includes a region 212 that includes a face and is not detected by thesecondary detector. In another example, box 206 includes a falsepositive region 213 that is not detected by the secondary detector.However, in the example illustrated, both the primary and secondarydetectors detect a region 210.

Region 210 has attributes 214 determined by the primary detector andattributes 216 determined by the secondary detector. Attributes 216include the left, top, right, and bottom sides of the region, thedetector score, and the landmarker score (described above). In additionthe parameters of attributes 216, attributes 214 further includes aprofile angle of the face in region 210 and an index of the camera thattook the image in a camera rosette.

At step 218, attributes 214 are used to generate the simple featurevector as described above. At step 222, the simple feature vector is fedinto a simple neural network. Based on the simple neural network, ascore is determined. If the score is below a threshold at step 224, thenthe face is not blurred at step 226. Otherwise, the composite featuregeneration and neural network are executed at steps 228-236. In thisway, the simple neural network filters out some of the regions withouthaving to use the more computationally expensive composite featuregeneration and neural network. Optionally, steps 218-226 may be foregoneentirely and the composite feature generation and neural network may beexecuted for every region.

The composite feature generation occurs at step 228. To generate acomposite feature vector, a face color histogram model and convolutionalneural network may be used as described above. The composite featurevector is inputted into a composite neural network as described above todetermine a score (step 230). If the score is below a threshold at step232, then the face is not blurred at step 236. Otherwise the face isblurred at step 234.

FIG. 3 is a flowchart illustrating a method 300 for detecting andblurring license plates in images, which may be used in operation of thesystem 100. Again, method 300 illustrates use of both a simple neuralnetwork and a composite neural network. In an alternative embodiment,only the composite neural network may be used.

Method 300 begins by detecting license plates at a step 301. In contrastto the two face detectors used above with respect to method 200, asingle license plate detector may be used. The license plate detectorgenerates attributes 302 describing each region. Attributes 302 mayinclude the left, top, right, and bottom sides of the region, thedetector score, whether the license plate is slanted or directly facingthe camera, and an index of the camera on the camera rosette that tookthe photo.

At step 322, a simple feature vector may be generated and the simplefeature vector may be inputted into a simple neural network as describedabove. The simple neural network determines a score. If the score isbelow a threshold at step 324, then the region is not blurred at step326. Otherwise, operation continues to composite feature generation atstep 304.

At step 304, a composite feature vector is determined. In addition tothe features (a) through (i) above, the feature vector may also includesinformation from a car detector for the image. This information providescontext for the neural network. The car detector may use a variety offeatures, including Haar features computed over pixel intensity, Harriscorner response, and gradient responses, and full-box gradient andintensity histograms. The training data for the car detector may begenerated automatically by expanding a region around a detected licenseplate proportionally. In operation of the feature generator, theproportions for training are again used to identify a car region thatcorresponds to a license plate region. Using the identified car regions,two features may be added to the feature vector. The first feature maybe the overlap (e.g. intersection over union) between the license plateregion and the car region. The second feature may be a score of the carregion generated by the car detector to indicate a level of confidencethat the car region includes a vehicle.

The feature vector is inputted into a composite neural network at step330. The composite neural network determines a score. If the score isbelow a threshold at step 332, the license plate region is not blurredin step 336; otherwise the license plate region is blurred at step 334.

FIG. 4 is a diagram 400 illustrating an example of detecting andblurring license plates in images. Diagram 400 includes an image thatincludes license plates of varying size. As indicated by the boxes indiagram 400, license plates have been detected. Further, the licenseplates have been blurred out.

The Summary and Abstract sections may set forth one or more but not allexemplary embodiments of the present invention as contemplated by theinventor(s), and thus, are not intended to limit the present inventionand the appended claims in any way.

The present invention has been described above with the aid offunctional building blocks illustrating the implementation of specifiedfunctions and relationships thereof. The boundaries of these functionalbuilding blocks have been arbitrarily defined herein for the convenienceof the description. Alternate boundaries can be defined so long as thespecified functions and relationships thereof are appropriatelyperformed.

The foregoing description of the specific embodiments will so fullyreveal the general nature of the invention that others can, by applyingknowledge within the skill of the art, readily modify and/or adapt forvarious applications such specific embodiments, without undueexperimentation, without departing from the general concept of thepresent invention. Therefore, such adaptations and modifications areintended to be within the meaning and range of equivalents of thedisclosed embodiments, based on the teaching and guidance presentedherein. It is to be understood that the phraseology or terminologyherein is for the purpose of description and not of limitation, suchthat the terminology or phraseology of the present specification is tobe interpreted by the skilled artisan in light of the teachings andguidance.

The breadth and scope of the present invention should not be limited byany of the above-described exemplary embodiments, but should be definedonly in accordance with the following claims and their equivalents.

What is claimed is:
 1. A system for detecting objects in an image,comprising: an object detector module configured to detect regions ofthe image based on at least the content of the image, wherein at leastsome of the regions include objects of a particular type; and a falsepositive detector module configured to determine whether each regiondetected by the object detector module does not include an object of theparticular type, based on at least a height of a camera with which theimage was taken and an estimated average height of objects of theparticular type, wherein the object detector module and false positivedetector module are implemented on at least one processor and memory. 2.The system of claim 1, wherein the object detector module is configuredto detect regions that include faces.
 3. The system of claim 1, whereinthe object detector module is configured to detect regions that includelicense plates.
 4. The system of claim 1, wherein the false positivedetector module is configured to determine whether each region detectedby the object detector module does not include an object of theparticular type at least based on information describing the shape ofthe region.
 5. The system of claim 1, wherein the false positivedetector module is configured to determine whether each region detectedby the object detector module does not include an object of theparticular type at least based on information describing the appearanceof the object in the region.
 6. The system of claim 1, wherein the falsepositive detector module is configured to determine whether each regiondetected by the object detector module does not include an object of theparticular type at least based on a degree to which the region overlapswith other regions in the image detected by the object detector module.7. The system of claim 1, wherein the image is taken from a camerarosette that takes panoramic images, and the information about thecontext in which the image was taken comprises information indicatingwhich camera in the camera rosette took the image.
 8. The system ofclaim 7, wherein the image is at least a portion of a street-levelpanoramic image.
 9. The system of claim 1, wherein the false positivedetector module comprises an artificial neural network.
 10. The systemof claim 9, wherein the artificial neural network is trained usingregions detected by the object detector module in other images.
 11. Thesystem of claim 1, further comprising: an image blurring moduleconfigured to blur the region of the image when the false positivedetector module determines that the region includes an object of theparticular type.
 12. A method for detecting objects in an image,comprising: (a) detecting, by one or more computing devices, regions ofthe image based on at least the content of the image, wherein at leastsome of the regions include objects of a particular type; and (b)determining, by one or more computing devices, whether each regiondetected by the object detector module does not include an object of theparticular type, based on at least a height of a camera with which theimage was taken and an estimated average height of objects of theparticular type.
 13. The method of claim 12, wherein the detecting (a)comprises detecting regions that include faces.
 14. The method of claim12, wherein the detecting (a) comprises detecting regions that includelicense plates.
 15. The method of claim 12, wherein the determining (b)comprises determining whether each region detected by the objectdetector module does not include an object of the particular type atleast based on information describing the shape of the region.
 16. Themethod of claim 12, wherein the determining (b) comprises determiningwhether each region detected by the object detector module does notinclude an object of the particular type at least based on informationdescribing the appearance of the object in the region.
 17. The method ofclaim 12, wherein the determining (b) comprises determining whether eachregion detected by the object detector module does not include an objectof the particular type at least based on a degree to which the regionoverlaps with other regions in the image detected by the object detectormodule.
 18. The method of claim 12, wherein the determining (b)comprises determining whether each region does not include an objectusing an artificial neural network, further comprising: (c) training theartificial neural network using regions detected by an object detectormodule in other images.
 19. The method of claim 12, further comprising:(c) training an object detector module to detect the regions at anincreased recall rate.